CELIBERTO JUNIOR, L. A.MATSMURA, J.Reinaldo Bianchi2022-01-122022-01-122007-12-03CELIBERTO JUNIOR, L. A.; MATSMURA, J.; BIANCHI, R. Heuristic Q-learning soccer players: A new reinforcement learning approach to RoboCup simulation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), p. 520-529, Dec. 2007.1611-3349https://repositorio.fei.edu.br/handle/FEI/4339This paper describes the design and implementation of a 4 player RoboCup Simulation 2D team, which was build by adding Heuristic: Accelerated Reinforcement Learning capabilities to basic players of the well-known UvA Trilearn team. The implemented agents learn by using a recently proposed Heuristic Heinforcement Learning algorithm, the Heuristically Accelerated Q-Learning (HAQL), which allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q-Learning. A set of empirical evaluations was conducted in the RoboCup 2D Simulator, and experimental results obtained while playing with other teams shows that the approach adopted hero is very promising. © Springer-Verlag Berlin Heidelberg 2007.Acesso RestritoArtigo de evento10.1007/978-3-540-77002-2_44Cognitive roboticsReinforcement learningRoboCup simulation 2D